AQUA: Automatic Collaborative Query Processing in Analytical Database
Summary: AQUA compiles collaborative relational–deep learning queries into optimizable SQL, avoiding opaque UDFs. It extends DL2SQL with declarative DL-data management and DL-specific optimizations, automating performance tuning and improving usability. (summarized by gpt-5.6-luna on Jul 24 2026)
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Authors
- 1. Yuchen Peng (Zhejiang University)
- 2. Ke Chen (Zhejiang University)
- 3. Lidan Shou (Zhejiang University)
- 4. Dawei Jiang (Zhejiang University)
- 5. Gang Chen (Zhejiang University)
BibTeX Citation
@article{peng_vldb23,
title = {{AQUA: Automatic Collaborative Query Processing in Analytical Database}},
author = {Peng, Yuchen and Chen, Ke and Shou, Lidan and Jiang, Dawei and Chen, Gang},
journal = {PVLDB},
series = {{VLDB} '23},
volume = {16},
number = {12},
pages = {4006--4009},
doi = {10.14778/3611540.3611607},
url = {https://doi.org/10.14778/3611540.3611607},
year = {2023}
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Showing 3 of 3 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 106 | The MADlib Analytics Library or MAD Skills, the SQL | 2012 | VLDB | 0.00033539462 |
| 2,347 | Vertica-ML: Distributed Machine Learning in Vertica Database | 2020 | SIGMOD | 8.7157552e-05 |
| 2,786 | DB4ML – An In-Memory Database Kernel with Machine Learning Support | 2020 | SIGMOD | 8.1207221e-05 |
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